Career Hunger’s Post

Dear Hiring team , why you are so focused on GenAI ? I mean , yes it's a good field , but why every 2nd company is so focused on GenAI ? Don't you think ML and DL are wonders behind everything ? Instead of "Hiring GenAI Engineers" who just needs to properly use APIs with minimal costs , you should focus on "Hiring ML Engineers" and "Hiring DL Engineers" ? And yes please pay a good CTC , coz good ML Engineers are all rounders in terms of SD and ML both . -- Just a thought -- could be wrong -- don't give me your opposing opinions -- I have ample time to send your arguments to dust :) #GenAI #AI #ML #DL #Hiring #Engineer #Technology #Tech

Pranav Kizhakkevellat Nair some roles are right up your alley. Please discuss and see how you can fit in approximately. Let me know how I can help you

Question for all of the ML Engineers and AI/ML Recruiters out there! I am currently in the process of learning how to recruit for AI/ML roles. My question for you: Are all ML Engineers trained to build Supervised and Unsupervised ML models, and, can there be specialization in either area when hiring an ML Engineer? Your insights will be greatly appreciated. #AIrecruitment #MLengineering #specialization

#hiring #urgenthiring #AIML #ML #AI Role: AI ML Location: San Diego, CA Interview mode: Teams & F2F Type of hiring: Full Time & Contract semiconductor experience Must Minimum Qualifications: Industry experience 10+ years BSCS, 5+ years MS/PhD in Computer Science, Statistics, Applied Math, or a related field Preferred Qualifications: Expert in AIML modeling, Python, AIML Infrastructure, model deployment and MLOps Experience working in Supply Chain, Operations, or a related field End to end GenAI application experience Knowledge in RAG, Prompt Engineering, LoRA and other GenAI techniques Ability to operate independently and lead without authority Superb communication and interpersonal skills   AI SW Engineer Skills/Experience: Experience in AI System Development foundational model Lifecycle training, retrieval of Augmented Systems, and Model Evaluation. Proficient in Large Language Models (LLMs) Architecture, creating targeted Instruction Datasets, and implementing Quantization and Inference Optimizations. Capable of covering Problem Definition, Data Acquisition, and Full-cycle Deployment, utilizing tools like MLflow, Cloud Platforms like AWS, Google Cloud, Azure. Proprietary Models like GPT-4 and Claude, Open-source Models like Llama and Mistral, able to deliver high-performing, scalable, and cost-efficient AI solutions. NLP libraries like NLTK, spaCy, GPT-3, BERT, and Hugging Face's Transformers to create sophisticated natural language processing solutions.   TECHNICAL SKILLS: Methodologies: Spiral, Agile, Waterfall, Lean Programming Language: C++, Python, Shell, SQL, R IDE Tools: PyCharm, RStudio, Visual Studio Code, Jupyter Notebook, Google Collab, Navicat ML Frameworks: Transformers, Scikit-Learn, Keras, TensorFlow, PyTorch, ONNX, NLTK, OpenAI, langchain, llama-index,kore.ai DL Architectures: LLM, ANN, CNN, R-CNN, RNN, GRU, LSTM, Transformers, Attention Mechanism, Tokenisers, BERT, T5, Sentence Transformers, Foundational models Packages: Pandas, NumPy, Spark, Matplotlib, SciPy Cloud Technologies: AWS (EC2, S3, RDS, ECS, Lambda), Azure(VM, Functions, ACR, AKS), Digital Ocean, GCP, Paperspace Database: MySQL, PostgreSQL, MongoDB, Chroma DB, Pinecone, SQLite Web Frameworks and Web Servers/ Deployment: Fast API, Flask, Nginx, Gunicorn, Uvicorn, Docker, Kubernetes Miscellaneous Tools and Technologies: JIRA, Auto Gluon, GitHub Actions, GitHub Issues, Datadog, MS Power BI, Postman, Locust, Stream lit, CUDA, cuDNN, TensorRT Version Control: Git, GitHub, DVC (Data Version Control) Operating Systems: Windows, Linux, Mac Harshith BLYK Engineering Services  Direct: ‪‪(562) 380-2884‬ Email: [email protected]

Careers in #Data and #AI do not have a clear path. Data and AI is a young and constantly changing field. There are so many different roles, it is easy to get lost in them. Especially because the definitions within different organizations are quite skewed. 💡 In general, the more mature the organization is in Data and AI, the more specialized roles you have. As an example, if a mature company is hiring an ML engineer, the candidate is expected to have a lot of knowledge of #MLOps and model deployment and mainly focus on those points. An immature company might be looking for an unicorn. For me, it is a red flag: the company has no idea what is needed to bring models to production, and a lot of changes are required before you can accomplish anything. #career

Valid point!! Effective collaboration and different skill set ensures in producing proper artifacts and streamlines different frameworks in AI product development lifecycle.

As AI is setting trends so wide it will surely take time to get settled with specialized domains.

We're hiring a Principal Machine Learning Engineer. If you're passionate about AI and innovation and your skills match the job requirements, send me your resume. I'll review and refer top candidates. Make sure to mention the following details in the message, along with your resume: - First Name - Last Name - Email Address - Years of Experience Check the job description and requirements in the link mentioned in the comments. #MachineLearning #AI #Hiring #TechCareers

In today's data-driven world, ML engineers are the unsung heroes behind cutting-edge innovations. For startups and enterprises alike, having the right ML engineer on board can mean the difference between success and failure. ML engineers play a crucial role in developing and implementing machine learning models that can: - Detect fraud: Safeguard your business from financial losses. - Assess risks: Make informed decisions based on data-driven insights. - Optimize strategies: Banks, insurance companies, and investment firms use ML for fraud detection, risk assessment, and algorithmic trading. Looking for an #ML engineer? Consider these key qualities: - Strong technical skills: Proficiency in Python, R, and ML frameworks like TensorFlow or PyTorch. - Problem-solving abilities: A knack for identifying and solving complex challenges. - Business acumen: Understanding how ML can drive value for your organization. - Communication skills: The ability to explain complex concepts to non-technical stakeholders. Don't settle for just any ML engineer. Invest in the right talent to propel your business forward. #MLEngineer #MachineLearning #DataScience #AI #ArtificialIntelligence HQ

Hiring artificial intelligence engineers: Why? (And some required skills!) Reasons why you company could need to hire an artificial intelligence engineer: - Data-Driven Decisions - Efficiency and Automation - Innovation and Competitivity  In a world where data is the new gold and efficiency is the key to success, an AI engineer can transform your business into a beacon of innovation. Are you ready for hiring your #AIEngineer and grow your team?